
import numpy as np
import pandas as pd
import statistics as stat
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import os
warnings.filterwarnings("ignore")


data=pd.read_csv('/Users/ankitmehra/Downloads/datasets/data.csv')
data


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18207 entries, 0 to 18206
Data columns (total 89 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Unnamed: 0                18207 non-null  int64  
 1   ID                        18207 non-null  int64  
 2   Name                      18207 non-null  object 
 3   Age                       18207 non-null  int64  
 4   Photo                     18207 non-null  object 
 5   Nationality               18207 non-null  object 
 6   Flag                      18207 non-null  object 
 7   Overall                   18207 non-null  int64  
 8   Potential                 18207 non-null  int64  
 9   Club                      17966 non-null  object 
 10  Club Logo                 18207 non-null  object 
 11  Value                     18207 non-null  object 
 12  Wage                      18207 non-null  object 
 13  Special                   18207 non-null  int64  
 14  Preferred Foot            18159 non-null  object 
 15  International Reputation  18159 non-null  float64
 16  Weak Foot                 18159 non-null  float64
 17  Skill Moves               18159 non-null  float64
 18  Work Rate                 18159 non-null  object 
 19  Body Type                 18159 non-null  object 
 20  Real Face                 18159 non-null  object 
 21  Position                  18147 non-null  object 
 22  Jersey Number             18147 non-null  float64
 23  Joined                    16654 non-null  object 
 24  Loaned From               1264 non-null   object 
 25  Contract Valid Until      17918 non-null  object 
 26  Height                    18159 non-null  object 
 27  Weight                    18159 non-null  object 
 28  LS                        16122 non-null  object 
 29  ST                        16122 non-null  object 
 30  RS                        16122 non-null  object 
 31  LW                        16122 non-null  object 
 32  LF                        16122 non-null  object 
 33  CF                        16122 non-null  object 
 34  RF                        16122 non-null  object 
 35  RW                        16122 non-null  object 
 36  LAM                       16122 non-null  object 
 37  CAM                       16122 non-null  object 
 38  RAM                       16122 non-null  object 
 39  LM                        16122 non-null  object 
 40  LCM                       16122 non-null  object 
 41  CM                        16122 non-null  object 
 42  RCM                       16122 non-null  object 
 43  RM                        16122 non-null  object 
 44  LWB                       16122 non-null  object 
 45  LDM                       16122 non-null  object 
 46  CDM                       16122 non-null  object 
 47  RDM                       16122 non-null  object 
 48  RWB                       16122 non-null  object 
 49  LB                        16122 non-null  object 
 50  LCB                       16122 non-null  object 
 51  CB                        16122 non-null  object 
 52  RCB                       16122 non-null  object 
 53  RB                        16122 non-null  object 
 54  Crossing                  18159 non-null  float64
 55  Finishing                 18159 non-null  float64
 56  HeadingAccuracy           18159 non-null  float64
 57  ShortPassing              18159 non-null  float64
 58  Volleys                   18159 non-null  float64
 59  Dribbling                 18159 non-null  float64
 60  Curve                     18159 non-null  float64
 61  FKAccuracy                18159 non-null  float64
 62  LongPassing               18159 non-null  float64
 63  BallControl               18159 non-null  float64
 64  Acceleration              18159 non-null  float64
 65  SprintSpeed               18159 non-null  float64
 66  Agility                   18159 non-null  float64
 67  Reactions                 18159 non-null  float64
 68  Balance                   18159 non-null  float64
 69  ShotPower                 18159 non-null  float64
 70  Jumping                   18159 non-null  float64
 71  Stamina                   18159 non-null  float64
 72  Strength                  18159 non-null  float64
 73  LongShots                 18159 non-null  float64
 74  Aggression                18159 non-null  float64
 75  Interceptions             18159 non-null  float64
 76  Positioning               18159 non-null  float64
 77  Vision                    18159 non-null  float64
 78  Penalties                 18159 non-null  float64
 79  Composure                 18159 non-null  float64
 80  Marking                   18159 non-null  float64
 81  StandingTackle            18159 non-null  float64
 82  SlidingTackle             18159 non-null  float64
 83  GKDiving                  18159 non-null  float64
 84  GKHandling                18159 non-null  float64
 85  GKKicking                 18159 non-null  float64
 86  GKPositioning             18159 non-null  float64
 87  GKReflexes                18159 non-null  float64
 88  Release Clause            16643 non-null  object 
dtypes: float64(38), int64(6), object(45)
memory usage: 12.4+ MB


# renaming columns


data.rename(columns = {'Unnamed: 0':'Duplicate'}, inplace = True)


# dropping duplicate columns


data.drop(['Photo','Flag','Club Logo','Duplicate'],axis=1,inplace=True)
data.shape

(18207, 85)


# data manipulation - columns conversion


data[['Value','Wage','Release Clause']]


def Value_float(Value):
    if isinstance(Value,str):
        out = Value.replace('€', '')
        if 'M' in out:
            out = float(out.replace('M', ''))*1000000
        elif 'K' in Value:
            out = float(out.replace('K', ''))*1000
        return float(out)
data['Value'] = data['Value'].apply(Value_float)
data['Wage'] = data['Wage'].apply(Value_float)
data['Release Clause'] = data['Release Clause'].apply(Value_float)


data[['Value','Wage','Release Clause']]


# target column - release clause class
def income(x):
    if x<1000000:
        return 'low_income_class'
    elif x>=1000000:
        return 'high_income_class'   
data["Release_Clause_Class"] = data["Release Clause"].apply(income)
data


data["Release Clause"].min()

13000.0


data["Release Clause"].max()

228100000.0


data["Release_Clause_Class"].value_counts()

high_income_class    9150
low_income_class     7493
Name: Release_Clause_Class, dtype: int64


sns.countplot(x="Release_Clause_Class",data=data)
plt.show()


data["Release_Clause_Class"] = data["Release_Clause_Class"].map({"low_income_class":0,
                                                                 "high_income_class":1})


data.head()


# converting columns


data['Joined'].isna().sum()

1553


data['Joined']=data['Joined'].fillna('0')


data['Joined'].isna().sum()

0


data['Joined'].head()

0     Jul 1, 2004
1    Jul 10, 2018
2     Aug 3, 2017
3     Jul 1, 2011
4    Aug 30, 2015
Name: Joined, dtype: object


def str4(x):
    if isinstance(x,str):
        return int(x[-4:])
data['Joined'] = data['Joined'].apply(str4)


data['Joined'].head()

0    2004
1    2018
2    2017
3    2011
4    2015
Name: Joined, dtype: int64


data['Contract Valid Until'].head()

0    2021
1    2022
2    2022
3    2020
4    2023
Name: Contract Valid Until, dtype: object


data['Contract Valid Until']= pd.to_datetime(data['Contract Valid Until'])


data['Contract Valid Until'].head()

0   2021-01-01
1   2022-01-01
2   2022-01-01
3   2020-01-01
4   2023-01-01
Name: Contract Valid Until, dtype: datetime64[ns]


data['Height'].head()

0     5'7
1     6'2
2     5'9
3     6'4
4    5'11
Name: Height, dtype: object


def feet(x):
    l=[]
    if isinstance(x,str):
        l = x.split("'")
        i = int(l[0])
        j = int(l[1])
        f = ((i*12)+j)/12
        return f
data['Height'] = data['Height'].apply(feet)


data['Height'].head()

0    5.583333
1    6.166667
2    5.750000
3    6.333333
4    5.916667
Name: Height, dtype: float64


data['Weight'].head()

0    159lbs
1    183lbs
2    150lbs
3    168lbs
4    154lbs
Name: Weight, dtype: object


def llbs(x):
    l=[]
    if isinstance(x,str):
        l = x.split("lbs")
        i = l[0]
        return float(i)
data['Weight'] = data['Weight'].apply(llbs)


data['Weight'].head()

0    159.0
1    183.0
2    150.0
3    168.0
4    154.0
Name: Weight, dtype: float64


# filling null values with mean


data['Weight'].fillna(data['Weight'].mean(), inplace = True)
data['ShortPassing'].fillna(data['ShortPassing'].mean(), inplace = True)
data['Volleys'].fillna(data['Volleys'].mean(), inplace = True)
data['Dribbling'].fillna(data['Dribbling'].mean(), inplace = True)
data['Curve'].fillna(data['Curve'].mean(), inplace = True)
data['FKAccuracy'].fillna(data['FKAccuracy'].mean(), inplace = True)
data['LongPassing'].fillna(data['LongPassing'].mean(), inplace = True)
data['BallControl'].fillna(data['BallControl'].mean(), inplace = True)
data['HeadingAccuracy'].fillna(data['HeadingAccuracy'].mean(), inplace = True)
data['Finishing'].fillna(data['Finishing'].mean(), inplace = True)
data['Crossing'].fillna(data['Crossing'].mean(), inplace = True)
data['Height'].fillna(data['Height'].mean(), inplace = True)
data['Joined'].fillna(data['Joined'].mean(), inplace = True)


# total number of unique positions and tables


data['Position'] = data['Position'].replace(np.nan, 0)
pos = list(data['Position'].unique()) 
print('Total number of unique positions:', len(pos)); print()
print('Positions:', pos)

Total number of unique positions: 28

Positions: ['RF', 'ST', 'LW', 'GK', 'RCM', 'LF', 'RS', 'RCB', 'LCM', 'CB', 'LDM', 'CAM', 'CDM', 'LS', 'LCB', 'RM', 'LAM', 'LM', 'LB', 'RDM', 'RW', 'CM', 'RB', 'RAM', 'CF', 'RWB', 'LWB', 0]


positiongroup = data.groupby(data['Position'])
tables = list()
for i in pos:
    a = positiongroup.get_group(i).sort_values('Overall',ascending=0).head(5)
    tables.append(a)
print('Total no. of tables:',len(tables))

Total no. of tables: 28


# repetition of players


idlist = list()
for i in range(0,len(pos)):
    a = tables[i]['ID']
    idlist.append(set(a.values))


ComIDs = []; x=0
while x<28:
    l = []; y=27
    while y>=0:
        if x != y:
            set1 = idlist[x].intersection(idlist[y])
            l.extend(list(set1))
            l = list(set(l))
        y -= 1
    ComIDs.extend(l)
    ComIDs = list(set(ComIDs))
    x += 1

if len(ComIDs)==0:
    print('There are no players appearing in more than one table.')
else:
    print('Players appearing in more than one table are:{}'.format(df1[df1['ID'] in ComIDs]))

There are no players appearing in more than one table.


# average wage for players in the corresponding tables


avgwage = list()
for i in range(0,len(pos)):
    m = tables[i]['Wage'].mean()
    avgwage.append(m)
    print('Average wage for the top 5 in {}: {}'.format(pos[i],avgwage[i]))

Average wage for the top 5 in RF: 148000.0
Average wage for the top 5 in ST: 294000.0
Average wage for the top 5 in LW: 261000.0
Average wage for the top 5 in GK: 192800.0
Average wage for the top 5 in RCM: 240800.0
Average wage for the top 5 in LF: 121200.0
Average wage for the top 5 in RS: 132200.0
Average wage for the top 5 in RCB: 231000.0
Average wage for the top 5 in LCM: 184400.0
Average wage for the top 5 in CB: 139600.0
Average wage for the top 5 in LDM: 126600.0
Average wage for the top 5 in CAM: 174000.0
Average wage for the top 5 in CDM: 217000.0
Average wage for the top 5 in LS: 130200.0
Average wage for the top 5 in LCB: 162000.0
Average wage for the top 5 in RM: 131400.0
Average wage for the top 5 in LAM: 81600.0
Average wage for the top 5 in LM: 164600.0
Average wage for the top 5 in LB: 177200.0
Average wage for the top 5 in RDM: 105000.0
Average wage for the top 5 in RW: 202000.0
Average wage for the top 5 in CM: 130600.0
Average wage for the top 5 in RB: 155400.0
Average wage for the top 5 in RAM: 45400.0
Average wage for the top 5 in CF: 47400.0
Average wage for the top 5 in RWB: 44200.0
Average wage for the top 5 in LWB: 34200.0
Average wage for the top 5 in 0: 0.0


top = data[data['Contract Valid Until']=='2020-01-01'].sort_values('Overall',ascending=0).head(20)
top


# average wage
avg = top['Wage'].mean(); print('Average wage for this set of players is:', avg)
# average age
age = top['Age'].mean(); print('Average age is:', age)
# correlation
top['Overall'].corr(top['Value'])

Average wage for this set of players is: 205450.0
Average age is: 30.65

0.7867166042074152


data2 = data.loc[data.Nationality=='Spain', 'International Reputation']
data2.value_counts()

1.0    932
2.0     94
3.0     36
4.0      9
Name: International Reputation, dtype: int64


data2.value_counts().plot(kind='pie', autopct='%1.2f%%', explode=[0,0,0.4,0.5])
plt.show()


data['Jersey Number'] = data['Jersey Number'].values.astype(int)
dfcorr = data[['Age', 'Overall', 'Potential', 'Finishing', 'ShotPower', 'LongShots', 'Penalties', 'FKAccuracy']].corr()
cmap = sns.color_palette('viridis')
f = sns.heatmap(dfcorr, annot = True, cmap = cmap)
f.set_title('Correlation of Shooting Attributes of Football Players', fontsize = 12)
plt.show()


d = sns.pairplot(data[['Age', 'Overall', 'Potential', 'Finishing', 'ShotPower', 'LongShots', 'Penalties', 'FKAccuracy']]);
d.fig.suptitle('Data distribution and correlation')
plt.show()


n = sns.distplot(data[data['Nationality'] == 'Brazil']['Potential']);
n.set_title('Potential for player in the league', fontsize = 12)
plt.show()


n = sns.countplot(data[data['Nationality'] == 'Portugal']['Overall'], palette='ocean_r');
n.set_title('Potential for player in the league(Portugal)', fontsize = 12)
plt.show()


columns=data[['Positioning', 'Finishing', 'ShotPower', 'LongShots', 'Penalties', 'Volleys']]
att_corr = columns.corr()
s = sns.heatmap(att_corr, annot = True)
s.set_title('Heat map for shooting ability')
plt.show()


from wordcloud import WordCloud  
#the name of the club
wordcloud = WordCloud(
                          background_color='lavenderblush',
                          width=1900,
                          height=1080
                         ).generate(" ".join(data.Club[0:30]))
plt.imshow(wordcloud)
plt.show()


wordcloud = WordCloud(
                          background_color='aliceblue',
                          width=1900,
                          height=1080
                         ).generate(" ".join(data.Name[0:30]))
plt.imshow(wordcloud)
plt.show()


sns.set(rc={"figure.figsize": (8, 6)})
plt.title("Overall ratings distribution")
ax = sns.distplot(data["Overall"],color="green",bins=10,kde=True)
plt.show()


# we observe that most of the players have mean ratings of 65 as most of the values are
# concentrated towards the neam and the graph follows a normal distribution by forming a bell
# shaped curve.


sns.set(rc={"figure.figsize": (20,10)})
sns.boxplot(x="Club", y="Age", data=top)
plt.title("Age group of different clubs")
plt.show()


sns.set(rc={"figure.figsize": (8, 6)})
plt.title("Distribution plot for Age Players ")
x=sns.distplot(x=data["Age"],color="green",bins=20,kde=True)
print("age of oldest player is : ", data["Age"].max())
print("age of youngest player is : ", data["Age"].min())
print("average age of players is : ", data["Age"].mean())
plt.show()

age of oldest player is :  45
age of youngest player is :  16
average age of players is :  25.122205745043114


sns.countplot("Nationality", data=data, order=data["Nationality"].value_counts().index[:5])
plt.title("Top 5 Countries that have maximum number of player participating in FIFA")
plt.show()


data["Nationality"].value_counts().head()

England      1662
Germany      1198
Spain        1072
Argentina     937
France        914
Name: Nationality, dtype: int64


sns.countplot("Preferred Foot", data=data)
plt.show()


plt.subplots(figsize=(20,8))
sns.countplot("Position", data=data, order=data["Position"].value_counts().index)
plt.xticks(rotation=45)
plt.title("Number of Positions")
plt.show()


plt.subplots(figsize=(20,8))
sns.countplot(x="Position",hue="Preferred Foot", data=data,order=data["Position"].value_counts().index)
plt.xticks(rotation=45)
plt.title("Preferred Foot for each position")
plt.show()


unique_counts = data.nunique()
lowest_distinct = unique_counts.min()
lowest_distinct_columns = unique_counts[unique_counts == lowest_distinct].index.tolist()
result = pd.DataFrame({col: data[col].unique() for col in lowest_distinct_columns})
result


data = data.dropna(subset=['Preferred Foot', 'Real Face', 'Release Clause'], how='all')


def height(x):
    if x>=5 and x<=6:
        return 'short'
    elif x>6:
        return 'tall'   
data["height_group"] = data["Height"].apply(height)
def w(x):
    if x>=100 and x<=150:
        return 'under'
    elif x>150 and x<=200:
        return 'average'
    elif x>200:
        return 'over'   
data["weight_group"] = data["Weight"].apply(w)
data


data["Height"].min()

5.083333333333333


data["Height"].max()

6.75


data[data["Height"]==5.083333333333333]


data[data["Height"]==6.75]


data["Weight"].min()

110.0


data["Weight"].max()

243.0


data[data["Weight"]==110.0]


data[data["Weight"]==243.0]


sns.scatterplot(x=data["Overall"],y=data["Potential"],data=data)
plt.show()


sns.scatterplot(x=data["Age"],y=data["Weight"],hue="weight_group",data=data)
plt.show()


sns.scatterplot(x=data["Age"],y=data["Height"],hue="height_group",data=data)
plt.show()


sns.jointplot(x="Value", y='Release Clause', data=data, kind="reg")
plt.show()


# STATISTICAL ANALYSIS FOR SIGNIFICANT COLUMNS WITH THE TARGET VARIABLE


data = data[data['Release Clause'].notna()]
data=data.drop(['Name',"Contract Valid Until",'Nationality','Club','Loaned From'],axis=1)
data.rename(columns = {'Release Clause':'Release_Clause', 
                       'International Reputation':'International_Reputation',
                      'Preferred Foot':'Preferred_Foot','Work Rate':'Work_Rate','Body Type':'Body_Type',
                      'Real Face':'Real_Face'}, inplace = True)
#Hypothesis : 
#Null Hypothesis : all columns have no relation with target column release clause.
#Alternative Hypothesis : all columns have relation with target column release clause.
import statsmodels.api as sm
from statsmodels.formula.api import ols
alpha=0.05


formula="Release_Clause~Preferred_Foot+Work_Rate+Body_Type+Real_Face+LS+ST+RS+LW+LF+CF+RF+RW+LAM+CAM+RAM+LM+LCM+CM+RCM+RM+LWB+LDM+CDM+RDM+RWB+LB+LCB+CB+RCB+RB"
model=ols(formula,data).fit()
anova_table=sm.stats.anova_lm(model)
anova_table


data.rename(columns = {'Skill Moves':'Skill_Moves', 
                       'Jersey Number':'Jersey_Number',"Weak Foot":"Weak_Foot"}, inplace = True)
formula="Release_Clause~ID+Age+Overall+Potential+Value+Wage+Special+International_Reputation+Weak_Foot+Skill_Moves+Jersey_Number+Joined+Height+Weight+Crossing+Finishing+HeadingAccuracy+ShortPassing+Volleys+Dribbling+Curve+FKAccuracy+LongPassing+BallControl+Acceleration+SprintSpeed+Agility+Reactions+Balance+ShotPower+Jumping+Stamina+Strength+LongShots+Aggression+Interceptions+Positioning+Vision+Penalties+Composure+Marking+StandingTackle+SlidingTackle+GKDiving+GKHandling+GKKicking+GKPositioning+GKReflexes+Release_Clause+Release_Clause_Class"
model1=ols(formula,data).fit()
anova_table=sm.stats.anova_lm(model1)
anova_table


# h0: there is no difference in population mean and sample mean 
#      (mu_population_mean=mu_sample_mean )
        #H0: mu>=4585060.98
# h1: there is no difference in population mean and sample mean 
#      (mu_population_mean!=mu_sample_mean)
        #H1: mu<4585060.98


data["Release_Clause"].mean()

4585060.986600974


x=data["Release_Clause"].sample(n=5000)
x.mean()

4588689.2


x=pd.DataFrame(x)
x.isna().sum()

Release_Clause    0
dtype: int64


x=x.dropna(how="all")


from scipy.stats import ttest_1samp,ttest_ind
from statsmodels.stats.power import ttest_power
print('Mean is %2.1f Sd is %2.1f' % (x["Release_Clause"].mean(),np.std(x["Release_Clause"],ddof = 1)))

Mean is 4588689.2 Sd is 10423659.0


t_statistic, p_value = ttest_1samp(x["Release_Clause"],4585060.98)
print(t_statistic, p_value)

0.024612652513128507 0.9803649094834173


alpha=0.05
pvalue=p_value
if pvalue<alpha:
    print("reject null and accept alternate")
else:
    print("fail to reject null")

fail to reject null


import scipy.stats as stats
mu = 4585060.98
xbar = x["Release_Clause"].mean()
n= 5000
s=np.std(x["Release_Clause"],ddof = 1)
std_error = s/np.sqrt(n)
t_stat = (xbar - mu)/std_error
t_crit=stats.t.ppf(0.025,n-1)
print( t_stat,t_crit)

0.02461265251312801 -1.9604386466615247


pvalue=stats.t.sf(t_stat,n-1)*2
pvalue

0.9803649094834177


import math
n=len(x["Release_Clause"])
xbar=np.mean(x["Release_Clause"])
pop_stdev=np.std(x["Release_Clause"],ddof=1)
z_critical=stats.norm.isf(0.05)
std_error=pop_stdev/math.sqrt(n)
marginal_error=z_critical*std_error
confidence_interval=(xbar-marginal_error,xbar+marginal_error)
print("confidence interval is : ",confidence_interval)

confidence interval is :  (4346216.722757076, 4831161.677242924)


from scipy.stats import f_oneway
from scipy.stats import ttest_1samp,ttest_ind
from statsmodels.stats.power import ttest_power
import matplotlib.pyplot as plt
import scipy
from statsmodels.formula.api import ols
import statsmodels.api as sm
from statsmodels.stats.anova import anova_lm
from scipy.stats import f_oneway


# h0: there is no difference in release clause of players who are short and tall in height group
#      (mu_tall=mu_short )
# h1: tthere is no difference in release clause of players who are short and tall in height group
#      (mu_tall!=mu_short )


# significance level
alpha=0.05
short=data[data["height_group"]=="short"]["Release_Clause"]
tall=data[data["height_group"]=="tall"]["Release_Clause"]
short=short.dropna(how="all")
tall=tall.dropna(how="all")
f_oneway(short,tall)

F_onewayResult(statistic=2.4869645622759378, pvalue=0.11481193892622046)


# using ols
formula="Release_Clause~height_group"
model=ols(formula,data).fit()
anova_table=anova_lm(model)
anova_table


# Using two sample independent ttest
t,p=stats.ttest_ind(short,tall)
print("test_statistic : ",t)
print("p value : ",p)

test_statistic :  -1.577011275253272
p value :  0.11481193892688972


# manual approach
alpha=0.05
n1=len(short)
n2=len(tall)
mean1=np.mean(short)
mean2=np.mean(tall)
sd1=np.std(short,ddof=1)
sd2=np.std(tall,ddof=1)
se1=sd1/np.sqrt(n1)
se2=sd2/np.sqrt(n2)
se=np.sqrt((se1**2+se2**2))
print("Standard Error:",se)
tstats=(mean1-mean2)/se
print("tstats:",tstats)
tcrit=stats.t.isf(alpha/2,n1+n2-2)
print("tcritical:",tcrit)
print("p value",round(2*stats.t.cdf(tstats,n1+n2-2),2))

Standard Error: 182738.55366884512
tstats: -1.5798464147935405
tcritical: 1.9601065505427373
p value 0.11


p_value=0.11
alpha=0.05
if pvalue<alpha:
    print("reject null and accept alternate")
else:
    print("fail to reject null")

fail to reject null


sns.barplot(x="height_group",y="Release_Clause",data=data)
plt.show()


n=len(short)
xbar=np.mean(short)
pop_stdev=np.std(short,ddof=1)
z_critical=stats.norm.isf(0.05)
std_error=pop_stdev/math.sqrt(n)
marginal_error=z_critical*std_error
confidence_interval=(xbar-marginal_error,xbar+marginal_error)
print("confidence interval is : ",confidence_interval)

confidence interval is :  (4315651.609226293, 4663033.694144494)


n=len(tall)
xbar=np.mean(tall)
pop_stdev=np.std(tall,ddof=1)
z_critical=stats.norm.isf(0.05)
std_error=pop_stdev/math.sqrt(n)
marginal_error=z_critical*std_error
confidence_interval=(xbar-marginal_error,xbar+marginal_error)
print("confidence interval is : ",confidence_interval)

confidence interval is :  (4532728.10970477, 5023354.891382581)


from scipy.stats import chi2_contingency


# h0: there is no difference in proportion of weight in short and tall players
# h1:there is difference in proportion of weight in short and tall players


z=pd.crosstab(data["weight_group"],data["height_group"])
z


array = np.array([[9173,5626],[14,355],[2938,53]])
array

array([[9173, 5626],
       [  14,  355],
       [2938,   53]])


chi_sq_Stat, p, deg_freedom, exp_freq = stats.chi2_contingency(array)
print('Chi-square statistic %3.5f P value %1.6f' %(chi_sq_Stat, p))

if p< alpha:
    print('Reject Null hypothesis and Accept alternate')
else:
    print('Fail to reject Null')

Chi-square statistic 2146.43783 P value 0.000000
Reject Null hypothesis and Accept alternate


pd.crosstab(data["weight_group"],data["height_group"]).plot(kind="bar",stacked=True)
plt.show()


data= data.apply(lambda x: x.fillna(x.value_counts().index[0]))


x=data.isna().sum()
y=x[x>0]
y

Series([], dtype: int64)


a=data.columns
print("all columns of the dataset are : \n ", a)
print("total number of columns in the dataset are : ", len(a))

all columns of the dataset are : 
  Index(['ID', 'Age', 'Overall', 'Potential', 'Value', 'Wage', 'Special',
       'Preferred_Foot', 'International_Reputation', 'Weak_Foot',
       'Skill_Moves', 'Work_Rate', 'Body_Type', 'Real_Face', 'Position',
       'Jersey_Number', 'Joined', 'Height', 'Weight', 'LS', 'ST', 'RS', 'LW',
       'LF', 'CF', 'RF', 'RW', 'LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM',
       'RM', 'LWB', 'LDM', 'CDM', 'RDM', 'RWB', 'LB', 'LCB', 'CB', 'RCB', 'RB',
       'Crossing', 'Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys',
       'Dribbling', 'Curve', 'FKAccuracy', 'LongPassing', 'BallControl',
       'Acceleration', 'SprintSpeed', 'Agility', 'Reactions', 'Balance',
       'ShotPower', 'Jumping', 'Stamina', 'Strength', 'LongShots',
       'Aggression', 'Interceptions', 'Positioning', 'Vision', 'Penalties',
       'Composure', 'Marking', 'StandingTackle', 'SlidingTackle', 'GKDiving',
       'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes',
       'Release_Clause', 'Release_Clause_Class', 'height_group',
       'weight_group'],
      dtype='object')
total number of columns in the dataset are :  83


cat=[col for col in data.columns if data[col].dtype=="object"]
print("categorical columns in the dataset are : ", cat,end=" ")
print("\ntotal number of categorical columns in the dataset are : ", len(cat))

categorical columns in the dataset are :  ['Preferred_Foot', 'Work_Rate', 'Body_Type', 'Real_Face', 'Position', 'LS', 'ST', 'RS', 'LW', 'LF', 'CF', 'RF', 'RW', 'LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM', 'RM', 'LWB', 'LDM', 'CDM', 'RDM', 'RWB', 'LB', 'LCB', 'CB', 'RCB', 'RB', 'height_group', 'weight_group'] 
total number of categorical columns in the dataset are :  33


num=[col for col in data.columns if data[col].dtype!="object"]
print("numerical columns in the dataset are : ", num,end=" ")
print("\ntotal number of numerical columns in the dataset are : ", len(num))

numerical columns in the dataset are :  ['ID', 'Age', 'Overall', 'Potential', 'Value', 'Wage', 'Special', 'International_Reputation', 'Weak_Foot', 'Skill_Moves', 'Jersey_Number', 'Joined', 'Height', 'Weight', 'Crossing', 'Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling', 'Curve', 'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration', 'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower', 'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression', 'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure', 'Marking', 'StandingTackle', 'SlidingTackle', 'GKDiving', 'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes', 'Release_Clause', 'Release_Clause_Class'] 
total number of numerical columns in the dataset are :  50


# for numeric columns
for cols in num:
    fig,axes = plt.subplots(1,2, figsize = (15,5))
    print("skewness for" , cols , "is : ", data[cols].skew())
    print("kurtosis for" , cols , "is : ", data[cols].kurtosis())
    sns.distplot(data[cols], bins = 25, ax = axes[0]).set(title = cols , ylabel = 'frequency')
    sns.boxplot(y = data[cols], ax = axes[1])
    plt.title(cols)
    plt.show()

skewness for ID is :  -2.2294887370832686
kurtosis for ID is :  9.215202613907739

skewness for Age is :  0.33737127650526305
kurtosis for Age is :  -0.5558199895438887

skewness for Overall is :  0.0840283889754774
kurtosis for Overall is :  0.0770865926594575

skewness for Potential is :  0.3135037536792178
kurtosis for Potential is :  0.07158722844751919

skewness for Value is :  7.001447183890468
kurtosis for Value is :  74.85414928231472

skewness for Wage is :  7.9694331748352925
kurtosis for Wage is :  101.42016347890001

skewness for Special is :  -0.693448105158702
kurtosis for Special is :  0.2936432400114133

skewness for International_Reputation is :  4.052396631154956
kurtosis for International_Reputation is :  18.85426492020848

skewness for Weak_Foot is :  0.1292694771471369
kurtosis for Weak_Foot is :  0.6596875547151875

skewness for Skill_Moves is :  0.1657353238072117
kurtosis for Skill_Moves is :  -0.07337433001886362


data=data.drop(['LS', 'ST', 'RS', 'LW', 'LF', 'CF', 'RF', 'RW', 'LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM',
                 'RCM', 'RM', 'LWB', 'LDM', 'CDM', 'RDM', 'RWB', 'LB', 'LCB', 'CB', 'RCB', 'RB'],axis=1)
data=pd.get_dummies(data,drop_first=True)
data.rename(columns = {'Release_Clause':'Release Clause', 
                       'International_Reputation':'International Reputation',
                      'Preferred_Foot':'Preferred Foot','Work_Rate':'Work Rate','Body_Type':'Body Type',
                      'Real_Face':'Real Face'}, inplace = True)
data.rename(columns = {'Skill_Moves':'Skill Moves', 
                       'Jersey_Number':'Jersey Number',"Weak_Foot":"Weak Foot"}, inplace = True)
import statsmodels.api as sm
X=data.drop("Release Clause",axis=1)
y=data["Release Clause"]


#Adding constant column of ones, mandatory for sm.OLS model
X_constant = sm.add_constant(X)
#Fitting sm.OLS model
model = sm.OLS(y,X_constant).fit()
model.summary()


data.shape

(16643, 98)


X_1 = sm.add_constant(X)
model = sm.OLS(y,X_1).fit()
model.pvalues

const                 5.586486e-01
ID                    8.585810e-01
Age                   8.738043e-03
Overall               3.077665e-14
Potential             1.547844e-23
                          ...     
Position_RWB          4.906341e-01
Position_ST           2.554567e-01
height_group_tall     2.984853e-01
weight_group_over     4.827601e-03
weight_group_under    5.788287e-01
Length: 98, dtype: float64


#Backward Elimination
cols = list(X.columns)
pmax = 1
while (len(cols)>0):
    p= []
    X_1 = X[cols]
    X_1 = sm.add_constant(X_1)
    model = sm.OLS(y,X_1).fit()
    p = pd.Series(model.pvalues.values[1:],index = cols)      
    pmax = max(p)
    feature_with_p_max = p.idxmax()
    if(pmax>0.05):
        cols.remove(feature_with_p_max)
    else:
        break
selected_features_BE = cols
print(selected_features_BE)

['Age', 'Overall', 'Potential', 'Value', 'Wage', 'International Reputation', 'Crossing', 'Finishing', 'ShortPassing', 'Volleys', 'LongPassing', 'Stamina', 'Vision', 'Marking', 'StandingTackle', 'GKHandling', 'Body_Type_C. Ronaldo', 'Body_Type_Courtois', 'Body_Type_Messi', 'Real_Face_Yes', 'Position_CDM', 'Position_GK', 'Position_LF', 'Position_LS', 'Position_RAM', 'Position_RF', 'weight_group_over']


len((selected_features_BE))

27


from sklearn.linear_model import LinearRegression
from  sklearn.model_selection import train_test_split


df=data[["Release Clause",'Age', 'Overall', 'Potential', 'Value', 'Wage', 'International Reputation', 'Crossing', 'Finishing', 'ShortPassing', 'Volleys', 'LongPassing', 'Stamina', 'Vision', 'Marking', 'StandingTackle', 'GKHandling', 'Body_Type_C. Ronaldo', 'Body_Type_Courtois', 'Body_Type_Messi', 'Real_Face_Yes', 'Position_CDM', 'Position_GK', 'Position_LF', 'Position_LS', 'Position_RAM', 'Position_RF', 'weight_group_over']]


X=df.drop("Release Clause",axis=1)
y=df["Release Clause"]


#Adding constant column of ones, mandatory for sm.OLS model
X_constant = sm.add_constant(X)
#Fitting sm.OLS model
model = sm.OLS(y,X_constant).fit()
model.summary()


from statsmodels.stats.outliers_influence import variance_inflation_factor
vif=pd.DataFrame()
vif["factor"]=[variance_inflation_factor(X_constant.values,i) for i in range (X_constant.shape[1])]
vif["features"]=X_constant.columns
vif


#MODEL 1 - SIMPLE LINEAR REGRESSION


X_train, X_test, y_train,y_test = train_test_split(X,y, test_size=0.3,random_state=42)
lr = LinearRegression()
lr.fit(X_train,y_train)
y_pred_train = lr.predict(X_train)
y_pred_test = lr.predict(X_test)
from sklearn.metrics import r2_score,mean_squared_error
print('R-squared on train data :',r2_score(y_train, y_pred_train))
print('R-squared on test data :',r2_score(y_test, y_pred_test))

print('\nRMSE on train data :', np.sqrt(mean_squared_error(y_train, y_pred_train)))
print('RMSE on test data :', np.sqrt(mean_squared_error(y_test, y_pred_test)))

R-squared on train data : 0.9901742271819832
R-squared on test data : 0.9887766278808289

RMSE on train data : 1094986.677280849
RMSE on test data : 1195383.387125873


Coefficients = pd.DataFrame({'Features':X.columns, 'Coefficient':lr.coef_})
Coefficients


Coefficients.sort_values(by="Coefficient",ascending=False)


# MODEL 2 Random Forest
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
lr=RandomForestRegressor()
lr.fit(X_train,y_train)
y_pred_train = lr.predict(X_train)
y_pred_test = lr.predict(X_test)
from sklearn.metrics import r2_score,mean_squared_error
print('R-squared on train data :',r2_score(y_train, y_pred_train))
print('R-squared on test data :',r2_score(y_test, y_pred_test))

print('\nRMSE on train data :', np.sqrt(mean_squared_error(y_train, y_pred_train)))
print('RMSE on test data :', np.sqrt(mean_squared_error(y_test, y_pred_test)))

R-squared on train data : 0.9980137020500968
R-squared on test data : 0.9872857597977909

RMSE on train data : 492320.2200635701
RMSE on test data : 1272303.5550455693


mse=np.mean((y_pred_test-y_test)**2)
rmse=np.sqrt(mse)
print("root_mean_squared_error :", rmse)
mae=(abs(y_pred_test-y_test)).mean()
print("mean_absolute_error :", mae)

root_mean_squared_error : 1272303.5550455693
mean_absolute_error : 435422.741838574


ypred=pd.DataFrame(y_pred_test,columns=["predicted"])
ytest=pd.DataFrame(y_test)
ytest=ytest.reset_index(drop=True)
new=pd.concat([ytest,ypred],axis=1)
new


# MODEL 3 Decision Tree
lr=DecisionTreeRegressor()
lr.fit(X_train,y_train)
y_pred_train = lr.predict(X_train)
y_pred_test = lr.predict(X_test)
from sklearn.metrics import r2_score,mean_squared_error
print('R-squared on train data :',r2_score(y_train, y_pred_train))
print('R-squared on test data :',r2_score(y_test, y_pred_test))

print('\nRMSE on train data :', np.sqrt(mean_squared_error(y_train, y_pred_train)))
print('RMSE on test data :', np.sqrt(mean_squared_error(y_test, y_pred_test)))

R-squared on train data : 1.0
R-squared on test data : 0.9827141181642447

RMSE on train data : 0.0
RMSE on test data : 1483512.8231380344


# MODEL 4 Cat Boost Regressor
from catboost import CatBoostRegressor
lr=CatBoostRegressor()
lr.fit(X_train,y_train)
y_pred_train = lr.predict(X_train)
y_pred_test = lr.predict(X_test)

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from sklearn.metrics import r2_score,mean_squared_error
print('R-squared on train data :',r2_score(y_train, y_pred_train))
print('R-squared on test data :',r2_score(y_test, y_pred_test))

print('\nRMSE on train data :', np.sqrt(mean_squared_error(y_train, y_pred_train)))
print('RMSE on test data :', np.sqrt(mean_squared_error(y_test, y_pred_test)))

R-squared on train data : 0.9985854521040535
R-squared on test data : 0.9860854898443139

RMSE on train data : 415464.86699676275
RMSE on test data : 1331004.4139728015


mse=np.mean((y_pred_test-y_test)**2)
rmse=np.sqrt(mse)
print("root_mean_squared_error :", rmse)
mae=(abs(y_pred_test-y_test)).mean()
print("mean_absolute_error :", mae)
mape=(abs(y_test-y_pred_test)/y_pred_test).mean()
print("mean_absolute_percentage_error :",mape)

root_mean_squared_error : 1331004.413972802
mean_absolute_error : 415131.58686873293
mean_absolute_percentage_error : 0.13107357468387298


ypred=pd.DataFrame(y_pred_test,columns=["predicted"])
ytest=pd.DataFrame(y_test)
ytest=ytest.reset_index(drop=True)
new=pd.concat([ytest,ypred],axis=1)


new["actual_predicted"]=new["predicted"].apply(lambda x: "%.7f" % x)
new["actual_predicted"]=new["actual_predicted"].astype(float)
new=new.drop(["predicted"],axis=1)
new["actual_predicted"]=new["actual_predicted"].apply(np.ceil)
new


model_cols=['Age', 'Overall', 'Potential', 'Value', 'Wage', 'International Reputation', 'Crossing', 'Finishing', 'ShortPassing', 'Volleys', 'LongPassing', 'Stamina', 'Vision', 'Marking', 'StandingTackle', 'GKHandling', 'Body_Type_C. Ronaldo', 'Body_Type_Courtois', 'Body_Type_Messi', 'Real_Face_Yes', 'Position_CDM', 'Position_GK', 'Position_LF', 'Position_LS', 'Position_RAM', 'Position_RF', 'weight_group_over']
multi_x=np.column_stack(tuple(df[col] for col in model_cols))
y=df["Release Clause"]


multi_train_x,multi_test_x,multi_train_y,multi_test_y=train_test_split(multi_x,y,test_size=0.3)
from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score
from sklearn import linear_model
import sklearn.metrics
multi_model=linear_model.LinearRegression()
multi_model.fit(multi_train_x,multi_train_y)
multi_model_intercept=multi_model.intercept_
multi_coefficient=dict(zip(model_cols,multi_model.coef_))


multi_model_intercept

733722.3940899568


multi_coefficient

{'Age': 14976.172397755767,
 'Overall': -42583.92784264459,
 'Potential': 34283.23541728564,
 'Value': 1.9748114573012572,
 'Wage': 1.85797218691539,
 'International Reputation': -770226.5824832785,
 'Crossing': 3739.8020137839135,
 'Finishing': 7786.308980215966,
 'ShortPassing': 3359.129594245497,
 'Volleys': -8560.178402658714,
 'LongPassing': -4005.48036774599,
 'Stamina': -7168.159601544103,
 'Vision': -1499.9872599295124,
 'Marking': -2966.9241259027817,
 'StandingTackle': 5882.758555169945,
 'GKHandling': 7474.662060547948,
 'Body_Type_C. Ronaldo': -22384854.269877747,
 'Body_Type_Courtois': 9961899.266395718,
 'Body_Type_Messi': 13125064.388448808,
 'Real_Face_Yes': 382650.9165839134,
 'Position_CDM': 141454.9857702136,
 'Position_GK': -368048.98571322876,
 'Position_LF': -2631879.124687559,
 'Position_LS': -499873.1838974353,
 'Position_RAM': -1783346.3295032077,
 'Position_RF': -2604157.3430842175,
 'weight_group_over': -130204.43485616088}


def predict_metrics(lr,x,y):
    pred=lr.predict(x)
    mae=mean_absolute_error(y,pred)
    mse=mean_squared_error(y,pred)
    r2=r2_score(y,pred)
    return mae,mse,r2
train_mae,train_mse,train_r2=predict_metrics(multi_model,multi_train_x,multi_train_y)
test_mae,test_mse,test_r2=predict_metrics(multi_model,multi_test_x,multi_test_y)
print(train_mae,train_mse,train_r2)
print(test_mae,test_mse,test_r2)

496907.47076216957 1242580881278.0315 0.9899351407614435
508168.5613845674 1280374552792.4653 0.9896739279163911


#RIDGE
from sklearn.linear_model import Ridge
r=Ridge(alpha=0.05,normalize=True)
r.fit(multi_train_x,multi_train_y)
train_mae,train_mse,train_r2=predict_metrics(r,multi_train_x,multi_train_y)
test_mae,test_mse,test_r2=predict_metrics(r,multi_test_x,multi_test_y)
print(train_mae,train_mse,train_r2)
print(test_mae,test_mse,test_r2)
#RMSE
print(np.sqrt(train_mse))
print(np.sqrt(test_mse))

697279.9742344046 2113903278659.7104 0.9828774615285003
689906.2573848163 1935353730947.1836 0.9843915969046276
1453926.8477676965
1391169.9144774457


#LASSO
from sklearn.linear_model import Lasso
lm=Lasso(alpha=0.05,normalize=True)
lm.fit(multi_train_x,multi_train_y)
train_mae,train_mse,train_r2=predict_metrics(lm,multi_train_x,multi_train_y)
test_mae,test_mse,test_r2=predict_metrics(lm,multi_test_x,multi_test_y)
print(train_mae,train_mse,train_r2)
print(test_mae,test_mse,test_r2)
#RMSE
print(np.sqrt(train_mse))
print(np.sqrt(test_mse))

496892.8851416481 1242580887898.9478 0.9899351407078143
508154.7968439397 1280368656243.724 0.989673975471372
1114711.1230713308
1131533.7627502433


#ELASTIC NET
from sklearn.linear_model import ElasticNet
e=ElasticNet(alpha=0.05)
e.fit(multi_train_x,multi_train_y)
train_mae,train_mse,train_r2=predict_metrics(e,multi_train_x,multi_train_y)
test_mae,test_mse,test_r2=predict_metrics(e,multi_test_x,multi_test_y)
print(train_mae,train_mse,train_r2)
print(test_mae,test_mse,test_r2)
#RMSE
print(np.sqrt(train_mse))
print(np.sqrt(test_mse))

496948.4984039156 1323348691796.6042 0.9892809244797317
500477.6516655898 1268535111613.285 0.9897694116346353
1150368.9372530032
1126292.6403085855


df=data[["Release Clause",'Age', 'Overall', 'Potential', 'Value', 'Wage', 'International Reputation', 'Crossing', 'Finishing', 'ShortPassing', 'Volleys', 'LongPassing', 'Stamina', 'Vision', 'Marking', 'StandingTackle', 'GKHandling', 'Body_Type_C. Ronaldo', 'Body_Type_Courtois', 'Body_Type_Messi', 'Real_Face_Yes', 'Position_CDM', 'Position_GK', 'Position_LF', 'Position_LS', 'Position_RAM', 'Position_RF', 'weight_group_over','Release_Clause_Class']]


df['Release_Clause_Class'].value_counts()

1.0    9150
0.0    7493
Name: Release_Clause_Class, dtype: int64


X=df.drop('Release_Clause_Class',axis=1)
y=df['Release_Clause_Class']
from sklearn.model_selection import train_test_split
from scipy.stats import chisquare
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42)
chisquare([len(y),len(y_train),len(y_test)])

Power_divergenceResult(statistic=6157.790001802559, pvalue=0.0)


from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
X_train=sc.fit_transform(X_train)
X_test=sc.fit_transform(X_test)
from sklearn.metrics import confusion_matrix,roc_auc_score,roc_curve,classification_report,accuracy_score


#Method 1
from sklearn.linear_model import LogisticRegression
classifier=LogisticRegression(random_state=42)
classifier.fit(X_train,y_train)
y_pred=classifier.predict(X_test)
from sklearn.metrics import confusion_matrix,roc_auc_score,roc_curve,classification_report,accuracy_score
cm=confusion_matrix(y_test,y_pred)
cm

array([[2157,   57],
       [ 164, 2615]])


#Method 2
from sklearn.neighbors import KNeighborsClassifier
classifier=KNeighborsClassifier(n_neighbors=5,metric="minkowski",p=2)
classifier.fit(X_train,y_train)
y_pred=classifier.predict(X_test)
cm=confusion_matrix(y_test,y_pred)
cm

array([[2053,  161],
       [ 228, 2551]])


# Method 3
from sklearn.tree import DecisionTreeClassifier
classifier=DecisionTreeClassifier(criterion="entropy",random_state=42)
classifier.fit(X_train,y_train)
y_pred=classifier.predict(X_test)
cm=confusion_matrix(y_test,y_pred)
cm

array([[2214,    0],
       [  94, 2685]])


# Method 4
from sklearn.ensemble import RandomForestClassifier
classi=RandomForestClassifier(n_estimators=50,criterion="entropy",random_state=42)
classi.fit(X_train,y_train)
y_pred=classi.predict(X_test)
cm=confusion_matrix(y_test,y_pred)
cm

array([[2214,    0],
       [  94, 2685]])


y_train_pred=classifier.predict(X_train)
y_train_prob=classifier.predict_proba(X_train)[:,1]
print("accuracy on train : ",accuracy_score(y_train,y_train_pred))
print("auc on train : ",roc_auc_score(y_train,y_train_prob))
y_test_pred=classifier.predict(X_test)
y_test_prob=classifier.predict_proba(X_test)[:,1]
print("accuracy on test : ",accuracy_score(y_test,y_test_pred))
print("auc on test : ",roc_auc_score(y_test,y_test_prob))

accuracy on train :  1.0
auc on train :  1.0
accuracy on test :  0.9811736431003405
auc on test :  0.9830874415257287


print(classification_report(y_test,y_pred))

              precision    recall  f1-score   support

         0.0       0.96      1.00      0.98      2214
         1.0       1.00      0.97      0.98      2779

    accuracy                           0.98      4993
   macro avg       0.98      0.98      0.98      4993
weighted avg       0.98      0.98      0.98      4993


print("confusion matrix : ","\n", confusion_matrix(y_train,y_train_pred))
print("confusion matrix : ","\n", confusion_matrix(y_test,y_test_pred))

confusion matrix :  
 [[5279    0]
 [   0 6371]]
confusion matrix :  
 [[2214    0]
 [  94 2685]]


cf=confusion_matrix(y_test,y_test_pred)
tn=cf[0,0]
fp=cf[0,1]
fn=cf[1,0]
tp=cf[1,1]
t=tn+fp+fn+tp
print(t)
s=(tp+tn)/t
print(s)
error=(fp+fn)/t
print(error)
sensitivity=tp/(fn+tp)
print(sensitivity) # recall score
specificity=tn/(tn+fp)
print(specificity)
precision=tp/(tp+fp)
print(precision)

4993
0.9811736431003405
0.018826356899659523
0.9661748830514574
1.0
1.0


y_prob=classi.predict_proba(X_test)
y_prob[0:10]

array([[0.  , 1.  ],
       [0.  , 1.  ],
       [0.  , 1.  ],
       [0.  , 1.  ],
       [0.94, 0.06],
       [0.  , 1.  ],
       [0.  , 1.  ],
       [0.94, 0.06],
       [0.96, 0.04],
       [0.  , 1.  ]])


y_prob[:,1]

array([1.  , 1.  , 1.  , ..., 0.  , 0.02, 1.  ])


fpr,tpr,thresholds=roc_curve(y_test,y_prob[:,1])
plt.plot(fpr,tpr)
plt.xlim([0.0,1.0])
plt.ylim([0.0,1.0])
plt.rcParams["font.size"]=12
plt.title("roc curve")
plt.xlabel("false positive rate")
plt.ylabel("true positive rate")
plt.grid(True)


from sklearn import metrics
print(metrics.accuracy_score(y_test,y_pred))
print(metrics.precision_score(y_test,y_pred))
print(metrics.recall_score(y_test,y_pred))

0.9811736431003405
1.0
0.9661748830514574


y_pred_proba=classi.predict_proba(X_test)[::,1]
fpr,tpr,_=metrics.roc_curve(y_test,y_pred_proba)
auc=metrics.roc_auc_score(y_test,y_pred_proba)
plt.plot(fpr,tpr,label="data 1 , auc ="+str(auc))
plt.legend(loc=4)
plt.show()


fpr , tpr , thresholds = roc_curve(y_train,y_train_prob)


plt.plot(fpr,tpr,color="r")
plt.plot(fpr,fpr,color="g")
plt.xlabel("FPR")
plt.ylabel("TPR")
plt.title("AUC")
plt.show()


thresholds[0]=thresholds[0]-1


fig,ax=plt.subplots(figsize=(10,5))
ax.plot(fpr,tpr,color="r")
ax.plot(fpr,fpr)
ax.set_xlabel("FPR")
ax.set_ylabel("TPR")
ax1=ax.twinx()
ax1.plot(fpr,thresholds)
ax1.set_ylabel("THRESHOLDS")
plt.show()


y_pred_class=y_prob[:,1]
sns.distplot(y_pred_class,kde=False)
plt.show()


for i in range(y_pred_class.size):
    print(y_pred_class[i],y_pred[i])

1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.06 0.0
1.0 1.0
1.0 1.0
0.06 0.0
0.04 0.0
1.0 1.0
0.02 0.0
0.0 0.0
1.0 1.0
0.96 1.0
0.0 0.0
0.96 1.0
0.0 0.0
1.0 1.0
1.0 1.0
0.98 1.0
0.02 0.0
0.0 0.0
1.0 1.0
0.02 0.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
0.96 1.0
0.0 0.0
1.0 1.0
0.02 0.0
0.0 0.0
0.96 1.0
1.0 1.0
1.0 1.0
0.96 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.94 1.0
1.0 1.0
0.88 1.0
0.06 0.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
0.98 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
0.02 0.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
0.98 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.96 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.94 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.92 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.98 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
0.02 0.0
0.0 0.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.98 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
0.06 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.02 0.0
0.0 0.0
0.02 0.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.98 1.0
0.22 0.0
1.0 1.0
0.98 1.0
1.0 1.0
1.0 1.0
0.08 0.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
0.92 1.0
1.0 1.0
0.0 0.0
0.08 0.0
0.0 0.0
0.86 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.08 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
0.96 1.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
0.98 1.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
0.02 0.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
0.14 0.0
0.02 0.0
0.14 0.0
0.98 1.0
0.0 0.0
0.96 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
0.02 0.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.08 0.0
0.16 0.0
0.02 0.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.96 1.0
0.0 0.0
1.0 1.0
0.06 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
0.96 1.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
0.0 0.0
0.04 0.0
0.02 0.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
0.98 1.0
1.0 1.0
0.08 0.0
1.0 1.0
0.98 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.98 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
0.02 0.0
0.02 0.0
0.04 0.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.98 1.0
0.94 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
0.08 0.0
1.0 1.0
0.0 0.0
1.0 1.0
0.02 0.0
1.0 1.0
1.0 1.0
0.04 0.0
0.0 0.0
1.0 1.0
0.0 0.0
0.02 0.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.96 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
0.06 0.0
0.02 0.0
0.02 0.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.94 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
0.02 0.0
0.02 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
0.1 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
0.02 0.0
0.98 1.0
1.0 1.0
0.02 0.0
1.0 1.0
0.0 0.0
1.0 1.0
0.98 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.94 1.0
1.0 1.0
1.0 1.0
0.76 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
0.0 0.0
0.08 0.0
0.0 0.0
1.0 1.0
1.0 1.0
0.0 0.0
0.04 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
0.0 0.0
0.06 0.0
0.0 0.0
1.0 1.0
0.92 1.0
0.92 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.98 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
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0.98 1.0
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0.96 1.0
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0.36 0.0
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0.0 0.0
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0.9 1.0
1.0 1.0
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1.0 1.0
0.98 1.0
1.0 1.0
1.0 1.0
0.98 1.0
1.0 1.0
0.0 0.0
0.0 0.0
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0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
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0.98 1.0
0.98 1.0
0.0 0.0
0.92 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.06 0.0
1.0 1.0
1.0 1.0
0.0 0.0
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1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
0.94 1.0
1.0 1.0
0.0 0.0
0.0 0.0
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0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
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1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
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1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
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1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
0.0 0.0
0.94 1.0
0.0 0.0
0.94 1.0
0.0 0.0
1.0 1.0
0.08 0.0
0.0 0.0
0.04 0.0
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1.0 1.0
1.0 1.0
1.0 1.0
0.08 0.0
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1.0 1.0
1.0 1.0
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1.0 1.0
1.0 1.0
0.96 1.0
0.0 0.0
1.0 1.0
0.18 0.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
0.04 0.0
0.96 1.0
0.0 0.0
0.02 0.0
1.0 1.0
0.04 0.0
1.0 1.0
1.0 1.0
0.1 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.94 1.0
0.06 0.0
1.0 1.0
0.98 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
0.04 0.0
0.0 0.0
1.0 1.0
0.9 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
0.02 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
0.94 1.0
0.04 0.0
0.98 1.0
1.0 1.0
0.02 0.0
0.02 0.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
0.14 0.0
0.98 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
0.98 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.94 1.0
1.0 1.0
0.0 0.0
0.12 0.0
0.0 0.0
0.02 0.0
1.0 1.0
0.92 1.0
0.98 1.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
0.0 0.0
0.02 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.94 1.0
0.96 1.0
1.0 1.0
0.9 1.0
0.04 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
0.12 0.0
0.0 0.0
0.02 0.0
1.0 1.0
0.0 0.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
0.02 0.0
0.0 0.0
0.02 0.0
0.0 0.0
1.0 1.0
0.94 1.0
1.0 1.0
1.0 1.0
0.84 1.0
0.98 1.0
0.0 0.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.98 1.0
0.04 0.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.88 1.0
1.0 1.0
0.0 0.0
1.0 1.0
0.0 0.0
1.0 1.0
1.0 1.0
1.0 1.0
1.0 1.0
0.0 0.0
0.0 0.0
1.0 1.0
0.0 0.0
0.84 1.0
0.12 0.0
0.0 0.0
0.98 1.0
0.18 0.0
1.0 1.0
0.0 0.0
0.0 0.0
0.02 0.0
1.0 1.0


y_pred_adj=y_pred
for i in range (y_pred_class.size):
    if y_pred_class[i]>0.3:
        y_pred_adj[i]=1
    else:
        y_pred_adj[i]=0


confusion_matrix(y_test,y_pred_adj)

array([[2214,    0],
       [  93, 2686]])


classi=RandomForestClassifier(max_depth=3,random_state=42)
classi.fit(X_train,y_train)
y_train_pred=classi.predict(X_train)
y_train_prob=classi.predict_proba(X_train)[:,1]
print("accuracy on train : ",accuracy_score(y_train,y_train_pred))
print("auc on train : ",roc_auc_score(y_train,y_train_prob))
print("confusion matrix : ","\n", confusion_matrix(y_train,y_train_pred))
y_test_pred=classi.predict(X_test)
y_test_prob=classi.predict_proba(X_test)[:,1]
print("accuracy on test : ",accuracy_score(y_test,y_test_pred))
print("auc on test : ",roc_auc_score(y_test,y_test_prob))
print("confusion matrix : ","\n", confusion_matrix(y_test,y_test_pred))

accuracy on train :  1.0
auc on train :  1.0
confusion matrix :  
 [[5279    0]
 [   0 6371]]
accuracy on test :  0.9811736431003405
auc on test :  0.9947563722368662
confusion matrix :  
 [[2214    0]
 [  94 2685]]


from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import randint as sp
z=RandomForestClassifier(random_state=42)
params={"n_estimators":sp(50,200),"max_features":sp(1,10),"max_depth":sp(2,10),"min_samples_split":sp(2,20),
       "min_samples_leaf":sp(1,20)}
rsearch=RandomizedSearchCV(z,param_distributions=params,n_iter=5,cv=3,scoring="roc_auc",random_state=42,
                          return_train_score=True,n_jobs=1)
rsearch.fit(X_train,y_train)

RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42),
                   n_iter=5, n_jobs=1,
                   param_distributions={'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839b3f0310>,
                                        'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839b3f09d0>,
                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839b3f0f10>,
                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839be80fd0>,
                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839b3f0b20>},
                   random_state=42, return_train_score=True, scoring='roc_auc')

RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42),
                   n_iter=5, n_jobs=1,
                   param_distributions={'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839b3f0310>,
                                        'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839b3f09d0>,
                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839b3f0f10>,
                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839be80fd0>,
                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839b3f0b20>},
                   random_state=42, return_train_score=True, scoring='roc_auc')

RandomForestClassifier(random_state=42)

RandomForestClassifier(random_state=42)


z=RandomForestClassifier(random_state=42)
params={"n_estimators":sp(50,200),"max_features":sp(1,10),"max_depth":sp(2,10),"min_samples_split":sp(2,20),
       "min_samples_leaf":sp(1,20)}
rsearch=RandomizedSearchCV(z,param_distributions=params,n_iter=10,cv=3,scoring="roc_auc",random_state=42,
                          return_train_score=True,n_jobs=1)
rsearch.fit(X_train,y_train)

RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42),
                   n_jobs=1,
                   param_distributions={'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839d63adc0>,
                                        'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839d63a280>,
                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839bedcca0>,
                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839becd850>,
                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839becd070>},
                   random_state=42, return_train_score=True, scoring='roc_auc')

RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42),
                   n_jobs=1,
                   param_distributions={'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839d63adc0>,
                                        'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839d63a280>,
                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839bedcca0>,
                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839becd850>,
                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f839becd070>},
                   random_state=42, return_train_score=True, scoring='roc_auc')

RandomForestClassifier(random_state=42)

RandomForestClassifier(random_state=42)


rsearch.best_params_

{'max_depth': 8,
 'max_features': 4,
 'min_samples_leaf': 15,
 'min_samples_split': 12,
 'n_estimators': 121}


rs=pd.DataFrame(rsearch.cv_results_)


rs.head(5)


rs.groupby("param_max_depth")["mean_test_score","mean_train_score"].agg("mean").plot()

<AxesSubplot:xlabel='param_max_depth'>


classi=RandomForestClassifier(**rsearch.best_params_)
classi.fit(X_train,y_train)

RandomForestClassifier(max_depth=8, max_features=4, min_samples_leaf=15,
                       min_samples_split=12, n_estimators=121)

RandomForestClassifier(max_depth=8, max_features=4, min_samples_leaf=15,
                       min_samples_split=12, n_estimators=121)


y_train_pred=classi.predict(X_train)
y_train_prob=classi.predict_proba(X_train)[:,1]
print("accuracy on train : ",accuracy_score(y_train,y_train_pred))
print("auc on train : ",roc_auc_score(y_train,y_train_prob))
print("confusion matrix : ","\n", confusion_matrix(y_train,y_train_pred))
y_test_pred=classi.predict(X_test)
y_test_prob=classi.predict_proba(X_test)[:,1]
print("accuracy on test : ",accuracy_score(y_test,y_test_pred))
print("auc on test : ",roc_auc_score(y_test,y_test_prob))
print("confusion matrix : ","\n", confusion_matrix(y_test,y_test_pred))

accuracy on train :  1.0
auc on train :  1.0
confusion matrix :  
 [[5279    0]
 [   0 6371]]
accuracy on test :  0.9811736431003405
auc on test :  0.9972751826594674
confusion matrix :  
 [[2214    0]
 [  94 2685]]


from sklearn.ensemble import AdaBoostClassifier
classi=AdaBoostClassifier()
classi.fit(X_train,y_train)
y_train_pred=classi.predict(X_train)
y_train_prob=classi.predict_proba(X_train)[:,1]
print("accuracy on train : ",accuracy_score(y_train,y_train_pred))
print("auc on train : ",roc_auc_score(y_train,y_train_prob))
print("confusion matrix : ","\n", confusion_matrix(y_train,y_train_pred))
y_test_pred=classi.predict(X_test)
y_test_prob=classi.predict_proba(X_test)[:,1]
print("accuracy on test : ",accuracy_score(y_test,y_test_pred))
print("auc on test : ",roc_auc_score(y_test,y_test_prob))
print("confusion matrix : ","\n", confusion_matrix(y_test,y_test_pred))

accuracy on train :  1.0
auc on train :  1.0
confusion matrix :  
 [[5279    0]
 [   0 6371]]
accuracy on test :  0.9811736431003405
auc on test :  0.9830874415257287
confusion matrix :  
 [[2214    0]
 [  94 2685]]


df.columns.get_loc("Release_Clause_Class")

28


array=df.values
X=array[:,[11,12,13,14,15,16,17,18,19,20]]
y=array[:,28]
models = []
models.append(('LR', LogisticRegression()))
models.append(('RF', RandomForestClassifier()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('DT', DecisionTreeClassifier()))


from sklearn import model_selection
results = []
names = []
scoring = 'accuracy'
for name, model in models:
    kfold = model_selection.KFold(n_splits=10, random_state=None)
    cv_results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring)
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)

LR: 0.679536 (0.159494)
RF: 0.775191 (0.160385)
KNN: 0.745928 (0.156370)
DT: 0.697926 (0.126725)


# boxplot algorithm comparison
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(211)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()

	Value	Wage	Release Clause
0	€110.5M	€565K	€226.5M
1	€77M	€405K	€127.1M
2	€118.5M	€290K	€228.1M
3	€72M	€260K	€138.6M
4	€102M	€355K	€196.4M
...	...	...	...
18202	€60K	€1K	€143K
18203	€60K	€1K	€113K
18204	€60K	€1K	€165K
18205	€60K	€1K	€143K
18206	€60K	€1K	€165K

	Value	Wage	Release Clause
0	110500000.0	565000.0	226500000.0
1	77000000.0	405000.0	127100000.0
2	118500000.0	290000.0	228100000.0
3	72000000.0	260000.0	138600000.0
4	102000000.0	355000.0	196400000.0
...	...	...	...
18202	60000.0	1000.0	143000.0
18203	60000.0	1000.0	113000.0
18204	60000.0	1000.0	165000.0
18205	60000.0	1000.0	143000.0
18206	60000.0	1000.0	165000.0

	df	sum_sq	mean_sq	F	PR(>F)
Preferred_Foot	1.0	9.211936e+13	9.211936e+13	12.270606	4.615968e-04
Work_Rate	8.0	6.436400e+16	8.045500e+15	1071.687445	0.000000e+00
Body_Type	9.0	1.434740e+17	1.594156e+16	2123.468358	0.000000e+00
Real_Face	1.0	3.323812e+17	3.323812e+17	44274.280951	0.000000e+00
LS	92.0	7.522404e+17	8.176527e+15	1089.140618	0.000000e+00
ST	92.0	1.411177e+17	1.533888e+15	204.318998	0.000000e+00
RS	92.0	5.935478e+16	6.451606e+14	85.937551	0.000000e+00
LW	104.0	1.153188e+17	1.108834e+15	147.700438	0.000000e+00
LF	101.0	2.325585e+16	2.302559e+14	30.670859	0.000000e+00
CF	101.0	1.603498e+16	1.587622e+14	21.147657	0.000000e+00
RF	101.0	1.254903e+16	1.242478e+14	16.550226	8.715449e-265
RW	104.0	1.951274e+16	1.876225e+14	24.991945	0.000000e+00
LAM	99.0	2.061221e+16	2.082041e+14	27.733482	0.000000e+00
CAM	99.0	5.757515e+15	5.815671e+13	7.746668	1.890903e-100
RAM	99.0	4.539872e+15	4.585729e+13	6.108344	1.217663e-71
LM	99.0	2.494914e+16	2.520115e+14	33.568768	0.000000e+00
LCM	91.0	1.247562e+16	1.370947e+14	18.261477	5.449000e-269
CM	91.0	3.627437e+15	3.986194e+13	5.309744	1.410508e-53
RCM	91.0	3.020967e+15	3.319744e+13	4.422010	5.523913e-40
RM	99.0	3.987367e+15	4.027644e+13	5.364956	6.666580e-59
LWB	94.0	2.530394e+16	2.691909e+14	35.857124	0.000000e+00
LDM	98.0	2.352521e+16	2.400532e+14	31.975881	0.000000e+00
CDM	98.0	2.025530e+15	2.066868e+13	2.753137	9.590235e-18
RDM	98.0	1.445686e+15	1.475190e+13	1.965002	4.633087e-08
RWB	94.0	1.684679e+15	1.792212e+13	2.387286	1.556629e-12
LB	97.0	6.937115e+15	7.151665e+13	9.526256	7.100463e-130
LCB	107.0	1.069029e+16	9.990925e+13	13.308245	3.320728e-216
CB	107.0	1.156577e+15	1.080913e+13	1.439812	2.064879e-03
RCB	107.0	1.231660e+15	1.151084e+13	1.533283	3.472934e-04
RB	97.0	1.584876e+15	1.633893e+13	2.176400	2.401565e-10
Residual	13880.0	1.042016e+17	7.507319e+12	NaN	NaN

	df	sum_sq	mean_sq	F	PR(>F)
ID	1.0	3.186724e+16	3.186724e+16	1.887392e+30	0.000000
Age	1.0	4.347045e+15	4.347045e+15	2.574612e+29	0.000000
Overall	1.0	8.811104e+17	8.811104e+17	5.218527e+31	0.000000
Potential	1.0	3.935338e+15	3.935338e+15	2.330771e+29	0.000000
Value	1.0	1.113597e+18	1.113597e+18	6.595466e+31	0.000000
Wage	1.0	1.367961e+12	1.367961e+12	8.101983e+25	0.000000
Special	1.0	6.879636e+12	6.879636e+12	4.074582e+26	0.000000
International_Reputation	1.0	4.767015e+14	4.767015e+14	2.823346e+28	0.000000
Weak_Foot	1.0	6.982612e+12	6.982612e+12	4.135571e+26	0.000000
Skill_Moves	1.0	2.099998e+12	2.099998e+12	1.243760e+26	0.000000
Jersey_Number	1.0	9.371773e+11	9.371773e+11	5.550593e+25	0.000000
Joined	1.0	1.409548e+12	1.409548e+12	8.348290e+25	0.000000
Height	1.0	3.852702e+11	3.852702e+11	2.281829e+25	0.000000
Weight	1.0	9.899069e+12	9.899069e+12	5.862893e+26	0.000000
Crossing	1.0	1.273477e+13	1.273477e+13	7.542386e+26	0.000000
Finishing	1.0	2.577620e+11	2.577620e+11	1.526640e+25	0.000000
HeadingAccuracy	1.0	2.057215e+12	2.057215e+12	1.218421e+26	0.000000
ShortPassing	1.0	4.869222e+12	4.869222e+12	2.883880e+26	0.000000
Volleys	1.0	4.935939e+13	4.935939e+13	2.923394e+27	0.000000
Dribbling	1.0	8.299397e+11	8.299397e+11	4.915459e+25	0.000000
Curve	1.0	2.385555e+12	2.385555e+12	1.412886e+26	0.000000
FKAccuracy	1.0	4.133019e+12	4.133019e+12	2.447851e+26	0.000000
LongPassing	1.0	2.423739e+12	2.423739e+12	1.435501e+26	0.000000
BallControl	1.0	1.404956e+11	1.404956e+11	8.321093e+24	0.000000
Acceleration	1.0	1.078754e+13	1.078754e+13	6.389102e+26	0.000000
SprintSpeed	1.0	1.251606e+12	1.251606e+12	7.412851e+25	0.000000
Agility	1.0	6.994169e+12	6.994169e+12	4.142416e+26	0.000000
Reactions	1.0	8.916389e+11	8.916389e+11	5.280884e+25	0.000000
Balance	1.0	4.852614e+11	4.852614e+11	2.874043e+25	0.000000
ShotPower	1.0	5.604731e+11	5.604731e+11	3.319498e+25	0.000000
Jumping	1.0	6.057224e+11	6.057224e+11	3.587495e+25	0.000000
Stamina	1.0	6.685164e+13	6.685164e+13	3.959402e+27	0.000000
Strength	1.0	1.536528e+12	1.536528e+12	9.100352e+25	0.000000
LongShots	1.0	1.564985e+11	1.564985e+11	9.268891e+24	0.000000
Aggression	1.0	5.726018e+11	5.726018e+11	3.391332e+25	0.000000
Interceptions	1.0	2.526161e+09	2.526161e+09	1.496162e+23	0.000000
Positioning	1.0	5.407311e+12	5.407311e+12	3.202572e+26	0.000000
Vision	1.0	9.000446e+12	9.000446e+12	5.330668e+26	0.000000
Penalties	1.0	5.567293e+12	5.567293e+12	3.297325e+26	0.000000
Composure	1.0	1.489117e+09	1.489117e+09	8.819552e+22	0.000000
Marking	1.0	4.481368e+12	4.481368e+12	2.654167e+26	0.000000
StandingTackle	1.0	1.326401e+13	1.326401e+13	7.855837e+26	0.000000
SlidingTackle	1.0	3.380766e+11	3.380766e+11	2.002316e+25	0.000000
GKDiving	1.0	9.100301e+10	9.100301e+10	5.389809e+24	0.000000
GKHandling	1.0	2.544972e+12	2.544972e+12	1.507303e+26	0.000000
GKKicking	1.0	4.993807e+10	4.993807e+10	2.957667e+24	0.000000
GKPositioning	1.0	3.515378e+11	3.515378e+11	2.082043e+25	0.000000
GKReflexes	1.0	5.480415e+10	5.480415e+10	3.245869e+24	0.000000
Release_Clause	1.0	2.180764e+16	2.180764e+16	1.291595e+30	0.000000
Release_Clause_Class	1.0	1.002670e-15	1.002670e-15	5.938485e-02	0.807474
Residual	16592.0	2.801439e-10	1.688427e-14	NaN	NaN

	df	sum_sq	mean_sq	F	PR(>F)
height_group	1.0	3.074257e+14	3.074257e+14	2.486965	0.114812
Residual	16641.0	2.057075e+18	1.236148e+14	NaN	NaN

	Unnamed: 0	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause
0	0	158023	L. Messi	31	https://cdn.sofifa.org/players/4/19/158023.png	Argentina	https://cdn.sofifa.org/flags/52.png	94	94	FC Barcelona	...	96.0	33.0	28.0	26.0	6.0	11.0	15.0	14.0	8.0	€226.5M
1	1	20801	Cristiano Ronaldo	33	https://cdn.sofifa.org/players/4/19/20801.png	Portugal	https://cdn.sofifa.org/flags/38.png	94	94	Juventus	...	95.0	28.0	31.0	23.0	7.0	11.0	15.0	14.0	11.0	€127.1M
2	2	190871	Neymar Jr	26	https://cdn.sofifa.org/players/4/19/190871.png	Brazil	https://cdn.sofifa.org/flags/54.png	92	93	Paris Saint-Germain	...	94.0	27.0	24.0	33.0	9.0	9.0	15.0	15.0	11.0	€228.1M
3	3	193080	De Gea	27	https://cdn.sofifa.org/players/4/19/193080.png	Spain	https://cdn.sofifa.org/flags/45.png	91	93	Manchester United	...	68.0	15.0	21.0	13.0	90.0	85.0	87.0	88.0	94.0	€138.6M
4	4	192985	K. De Bruyne	27	https://cdn.sofifa.org/players/4/19/192985.png	Belgium	https://cdn.sofifa.org/flags/7.png	91	92	Manchester City	...	88.0	68.0	58.0	51.0	15.0	13.0	5.0	10.0	13.0	€196.4M
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18202	18202	238813	J. Lundstram	19	https://cdn.sofifa.org/players/4/19/238813.png	England	https://cdn.sofifa.org/flags/14.png	47	65	Crewe Alexandra	...	45.0	40.0	48.0	47.0	10.0	13.0	7.0	8.0	9.0	€143K
18203	18203	243165	N. Christoffersson	19	https://cdn.sofifa.org/players/4/19/243165.png	Sweden	https://cdn.sofifa.org/flags/46.png	47	63	Trelleborgs FF	...	42.0	22.0	15.0	19.0	10.0	9.0	9.0	5.0	12.0	€113K
18204	18204	241638	B. Worman	16	https://cdn.sofifa.org/players/4/19/241638.png	England	https://cdn.sofifa.org/flags/14.png	47	67	Cambridge United	...	41.0	32.0	13.0	11.0	6.0	5.0	10.0	6.0	13.0	€165K
18205	18205	246268	D. Walker-Rice	17	https://cdn.sofifa.org/players/4/19/246268.png	England	https://cdn.sofifa.org/flags/14.png	47	66	Tranmere Rovers	...	46.0	20.0	25.0	27.0	14.0	6.0	14.0	8.0	9.0	€143K
18206	18206	246269	G. Nugent	16	https://cdn.sofifa.org/players/4/19/246269.png	England	https://cdn.sofifa.org/flags/14.png	46	66	Tranmere Rovers	...	43.0	40.0	43.0	50.0	10.0	15.0	9.0	12.0	9.0	€165K

	ID	Name	Age	Nationality	Overall	Potential	Club	Value	Wage	Special	...	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause	Release_Clause_Class	height_group	weight_group
6275	237994	N. Barrios	20	Argentina	69	82	San Lorenzo de Almagro	2000000.0	7000.0	1687	...	51.0	13.0	8.0	7.0	12.0	6.0	4300000.0	1.0	short	under
6584	232951	H. Nakagawa	23	Japan	69	72	Kashiwa Reysol	1300000.0	5000.0	1740	...	31.0	15.0	10.0	7.0	12.0	13.0	1800000.0	1.0	short	under
17157	242566	K. Yamaguchi	23	Japan	55	61	Shonan Bellmare	120000.0	1000.0	1383	...	13.0	14.0	5.0	15.0	9.0	14.0	174000.0	0.0	short	under

	ID	Name	Age	Nationality	Overall	Potential	Club	Value	Wage	Special	...	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause	Release_Clause_Class	height_group	weight_group
11614	199321	T. Holý	26	Czech Republic	64	68	Gillingham	400000.0	2000.0	1028	...	25.0	68.0	58.0	57.0	63.0	68.0	730000.0	0.0	tall	over
17927	243796	D. Hodzic	22	Croatia	51	61	Vejle Boldklub	50000.0	1000.0	793	...	11.0	50.0	47.0	56.0	52.0	58.0	73000.0	0.0	tall	average

	ID	Name	Age	Nationality	Overall	Potential	Club	Value	Wage	Special	...	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause	Release_Clause_Class	height_group	weight_group
13338	235600	B. Al Mutairi	28	Saudi Arabia	62	62	Al Batin	240000.0	3000.0	1522	...	56.0	7.0	6.0	13.0	8.0	9.0	396000.0	0.0	short	under
17157	242566	K. Yamaguchi	23	Japan	55	61	Shonan Bellmare	120000.0	1000.0	1383	...	13.0	14.0	5.0	15.0	9.0	14.0	174000.0	0.0	short	under

Dep. Variable:	Release Clause	R-squared:	0.990
Model:	OLS	Adj. R-squared:	0.990
Method:	Least Squares	F-statistic:	1.679e+04
Date:	Mon, 15 Aug 2022	Prob (F-statistic):	0.00
Time:	22:22:22	Log-Likelihood:	-2.5536e+05
No. Observations:	16643	AIC:	5.109e+05
Df Residuals:	16545	BIC:	5.117e+05
Df Model:	97
Covariance Type:	nonrobust

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	-5.201e+06	8.89e+06	-0.585	0.559	-2.26e+07	1.22e+07
ID	-0.0831	0.466	-0.178	0.859	-0.997	0.831
Age	1.301e+04	4959.777	2.622	0.009	3285.065	2.27e+04
Overall	-4.246e+04	5585.682	-7.602	0.000	-5.34e+04	-3.15e+04
Potential	3.633e+04	3628.376	10.014	0.000	2.92e+04	4.34e+04
Value	1.9596	0.004	532.978	0.000	1.952	1.967
Wage	2.4113	0.848	2.842	0.004	0.748	4.074
Special	1095.8467	5051.299	0.217	0.828	-8805.241	1.1e+04
International Reputation	-6.919e+05	3.33e+04	-20.760	0.000	-7.57e+05	-6.27e+05
Weak Foot	-1.554e+04	1.45e+04	-1.072	0.284	-4.39e+04	1.29e+04
Skill Moves	3.279e+04	2.41e+04	1.363	0.173	-1.44e+04	7.99e+04
Jersey Number	446.9301	574.130	0.778	0.436	-678.427	1572.287
Joined	2429.8789	4365.461	0.557	0.578	-6126.894	1.1e+04
Height	1.504e+05	9.42e+04	1.597	0.110	-3.42e+04	3.35e+05
Weight	-1801.5865	1204.152	-1.496	0.135	-4161.853	558.680
Crossing	1787.2234	5223.807	0.342	0.732	-8451.999	1.2e+04
Finishing	6449.7258	5269.061	1.224	0.221	-3878.199	1.68e+04
HeadingAccuracy	130.9172	5215.502	0.025	0.980	-1.01e+04	1.04e+04
ShortPassing	4087.7899	5487.287	0.745	0.456	-6667.881	1.48e+04
Volleys	-7880.6693	5209.614	-1.513	0.130	-1.81e+04	2330.734
Dribbling	-1512.9219	5408.071	-0.280	0.780	-1.21e+04	9087.478
Curve	1339.0664	5208.602	0.257	0.797	-8870.353	1.15e+04
FKAccuracy	-3209.9265	5185.384	-0.619	0.536	-1.34e+04	6953.982
LongPassing	-4386.4750	5295.865	-0.828	0.408	-1.48e+04	5993.989
BallControl	362.3062	5560.860	0.065	0.948	-1.05e+04	1.13e+04
Acceleration	-1799.5142	5347.082	-0.337	0.736	-1.23e+04	8681.340
SprintSpeed	-979.7666	5290.747	-0.185	0.853	-1.14e+04	9390.666
Agility	-3468.8236	5231.747	-0.663	0.507	-1.37e+04	6785.962
Reactions	-1260.4322	5399.195	-0.233	0.815	-1.18e+04	9322.571
Balance	-277.8233	5218.333	-0.053	0.958	-1.05e+04	9950.671
ShotPower	-1747.1652	5226.487	-0.334	0.738	-1.2e+04	8497.310
Jumping	-1023.5525	5134.756	-0.199	0.842	-1.11e+04	9041.121
Stamina	-7123.1016	5160.703	-1.380	0.168	-1.72e+04	2992.431
Strength	-1837.4149	5210.988	-0.353	0.724	-1.21e+04	8376.681
LongShots	718.0344	5236.652	0.137	0.891	-9546.365	1.1e+04
Aggression	-232.9794	5159.012	-0.045	0.964	-1.03e+04	9879.237
Interceptions	-3466.4239	5249.463	-0.660	0.509	-1.38e+04	6823.088
Positioning	-2030.4882	5266.996	-0.386	0.700	-1.24e+04	8293.389
Vision	-3283.3262	5206.216	-0.631	0.528	-1.35e+04	6921.415
Penalties	-2651.0799	5183.743	-0.511	0.609	-1.28e+04	7509.613
Composure	315.7233	1457.006	0.217	0.828	-2540.164	3171.611
Marking	-4089.9889	5188.342	-0.788	0.431	-1.43e+04	6079.719
StandingTackle	3931.1238	5474.396	0.718	0.473	-6799.279	1.47e+04
SlidingTackle	285.7604	5421.581	0.053	0.958	-1.03e+04	1.09e+04
GKDiving	1700.9705	5716.385	0.298	0.766	-9503.759	1.29e+04
GKHandling	5798.2585	5752.640	1.008	0.314	-5477.533	1.71e+04
GKKicking	1955.7878	5648.621	0.346	0.729	-9116.116	1.3e+04
GKPositioning	2202.8287	5677.591	0.388	0.698	-8925.860	1.33e+04
GKReflexes	904.3443	5711.569	0.158	0.874	-1.03e+04	1.21e+04
Release_Clause_Class	-5.179e+04	2.96e+04	-1.750	0.080	-1.1e+05	6232.274
Preferred_Foot_Right	4106.1389	2.44e+04	0.169	0.866	-4.36e+04	5.19e+04
Work_Rate_High/ Low	5.801e+04	6.11e+04	0.950	0.342	-6.17e+04	1.78e+05
Work_Rate_High/ Medium	8.366e+04	4.36e+04	1.920	0.055	-1767.169	1.69e+05
Work_Rate_Low/ High	2.334e+04	7.09e+04	0.329	0.742	-1.16e+05	1.62e+05
Work_Rate_Low/ Low	3.08e+05	2.2e+05	1.398	0.162	-1.24e+05	7.4e+05
Work_Rate_Low/ Medium	7.479e+04	7.11e+04	1.053	0.293	-6.45e+04	2.14e+05
Work_Rate_Medium/ High	8.652e+04	4.87e+04	1.776	0.076	-8985.880	1.82e+05
Work_Rate_Medium/ Low	7.765e+04	5.82e+04	1.335	0.182	-3.64e+04	1.92e+05
Work_Rate_Medium/ Medium	5.918e+04	4.18e+04	1.416	0.157	-2.27e+04	1.41e+05
Body_Type_C. Ronaldo	-2.199e+07	1.62e+06	-13.581	0.000	-2.52e+07	-1.88e+07
Body_Type_Courtois	1.025e+07	1.61e+06	6.368	0.000	7.1e+06	1.34e+07
Body_Type_Lean	-6.019e+04	1.15e+06	-0.052	0.958	-2.31e+06	2.19e+06
Body_Type_Messi	1.316e+07	1.67e+06	7.901	0.000	9.89e+06	1.64e+07
Body_Type_Neymar	-2.015e+06	1.63e+06	-1.240	0.215	-5.2e+06	1.17e+06
Body_Type_Normal	-7.964e+04	1.15e+06	-0.069	0.945	-2.33e+06	2.17e+06
Body_Type_PLAYER_BODY_TYPE_25	1.715e+06	1.61e+06	1.065	0.287	-1.44e+06	4.87e+06
Body_Type_Shaqiri	4.902e+04	1.61e+06	0.031	0.976	-3.1e+06	3.2e+06
Body_Type_Stocky	-5.827e+04	1.15e+06	-0.051	0.960	-2.31e+06	2.19e+06
Real_Face_Yes	3.713e+05	3.69e+04	10.065	0.000	2.99e+05	4.44e+05
Position_CB	1.525e+05	6.99e+04	2.181	0.029	1.55e+04	2.89e+05
Position_CDM	2.061e+05	6.34e+04	3.249	0.001	8.17e+04	3.3e+05
Position_CF	-2.027e+05	1.44e+05	-1.406	0.160	-4.85e+05	7.99e+04
Position_CM	7.695e+04	5.46e+04	1.409	0.159	-3.01e+04	1.84e+05
Position_GK	-6.701e+05	1.94e+05	-3.461	0.001	-1.05e+06	-2.91e+05
Position_LAM	-3.511e+05	2.55e+05	-1.378	0.168	-8.51e+05	1.48e+05
Position_LB	1.38e+05	6.52e+04	2.116	0.034	1.02e+04	2.66e+05
Position_LCB	1.258e+05	7.96e+04	1.580	0.114	-3.02e+04	2.82e+05
Position_LCM	7.05e+04	7.44e+04	0.948	0.343	-7.53e+04	2.16e+05
Position_LDM	1.147e+05	8.92e+04	1.286	0.199	-6.01e+04	2.89e+05
Position_LF	-2.705e+06	2.94e+05	-9.213	0.000	-3.28e+06	-2.13e+06
Position_LM	1.024e+05	5.33e+04	1.923	0.055	-1981.401	2.07e+05
Position_LS	-1.61e+05	9.68e+04	-1.664	0.096	-3.51e+05	2.86e+04
Position_LW	1.648e+05	7.36e+04	2.240	0.025	2.06e+04	3.09e+05
Position_LWB	9.025e+04	1.4e+05	0.646	0.518	-1.83e+05	3.64e+05
Position_RAM	-1.038e+06	2.49e+05	-4.177	0.000	-1.53e+06	-5.51e+05
Position_RB	1.604e+05	6.44e+04	2.489	0.013	3.41e+04	2.87e+05
Position_RCB	1.476e+05	7.99e+04	1.848	0.065	-8915.651	3.04e+05
Position_RCM	7.207e+04	7.5e+04	0.961	0.337	-7.5e+04	2.19e+05
Position_RDM	1.932e+05	8.91e+04	2.168	0.030	1.85e+04	3.68e+05
Position_RF	-1.675e+06	3.26e+05	-5.136	0.000	-2.31e+06	-1.04e+06
Position_RM	1.111e+05	5.34e+04	2.080	0.038	6384.441	2.16e+05
Position_RS	4.391e+04	9.76e+04	0.450	0.653	-1.47e+05	2.35e+05
Position_RW	3.307e+04	7.47e+04	0.443	0.658	-1.13e+05	1.79e+05
Position_RWB	9.172e+04	1.33e+05	0.689	0.491	-1.69e+05	3.53e+05
Position_ST	6.502e+04	5.72e+04	1.137	0.255	-4.7e+04	1.77e+05
height_group_tall	-3.246e+04	3.12e+04	-1.040	0.298	-9.36e+04	2.87e+04
weight_group_over	-1.962e+05	6.96e+04	-2.819	0.005	-3.33e+05	-5.98e+04
weight_group_under	-1.829e+04	3.29e+04	-0.555	0.579	-8.29e+04	4.63e+04

Omnibus:	6421.087	Durbin-Watson:	1.959
Prob(Omnibus):	0.000	Jarque-Bera (JB):	1630805.547
Skew:	-0.635	Prob(JB):	0.00
Kurtosis:	51.478	Cond. No.	6.39e+09

Omnibus:	6380.743	Durbin-Watson:	1.954
Prob(Omnibus):	0.000	Jarque-Bera (JB):	1620409.451
Skew:	-0.621	Prob(JB):	0.00
Kurtosis:	51.324	Cond. No.	8.67e+08

	factor	features
0	394.483982	const
1	4.685931	Age
2	10.120906	Overall
3	6.113049	Potential
4	5.101512	Value
5	4.617565	Wage
6	2.238477	International Reputation
7	3.866488	Crossing
8	7.423025	Finishing
9	11.422243	ShortPassing
10	5.607154	Volleys
11	6.988180	LongPassing
12	3.229543	Stamina
13	3.670714	Vision
14	6.499113	Marking
15	7.407469	StandingTackle
16	22.372930	GKHandling
17	1.028475	Body_Type_C. Ronaldo
18	1.014689	Body_Type_Courtois
19	1.144733	Body_Type_Messi
20	1.459145	Real_Face_Yes
21	1.107730	Position_CDM
22	23.897547	Position_GK
23	1.010669	Position_LF
24	1.041567	Position_LS
25	1.009110	Position_RAM
26	1.086007	Position_RF
27	1.057891	weight_group_over

	Features	Coefficient
0	Age	1.597535e+04
1	Overall	-4.338150e+04
2	Potential	3.624759e+04
3	Value	1.967426e+00
4	Wage	1.613862e+00
5	International Reputation	-7.280892e+05
6	Crossing	2.973803e+03
7	Finishing	6.320073e+03
8	ShortPassing	4.343190e+03
9	Volleys	-6.563284e+03
10	LongPassing	-3.740367e+03
11	Stamina	-7.180118e+03
12	Vision	-2.883650e+03
13	Marking	-4.289760e+03
14	StandingTackle	7.157875e+03
15	GKHandling	9.900991e+03
16	Body_Type_C. Ronaldo	-2.190050e+07
17	Body_Type_Courtois	2.328306e-08
18	Body_Type_Messi	-3.725290e-09
19	Real_Face_Yes	3.764197e+05
20	Position_CDM	9.993918e+04
21	Position_GK	-4.706803e+05
22	Position_LF	-1.702166e+06
23	Position_LS	-2.189129e+05
24	Position_RAM	-6.739480e+05
25	Position_RF	-1.948272e+06
26	weight_group_over	-2.492186e+05

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	4.664e+05	1.72e+05	2.709	0.007	1.29e+05	8.04e+05
Age	1.38e+04	3979.209	3.468	0.001	5999.532	2.16e+04
Overall	-4.219e+04	3935.752	-10.720	0.000	-4.99e+04	-3.45e+04
Potential	3.618e+04	3484.826	10.381	0.000	2.93e+04	4.3e+04
Value	1.9598	0.003	572.504	0.000	1.953	1.967
Wage	2.4106	0.837	2.881	0.004	0.770	4.051
International Reputation	-6.905e+05	3.25e+04	-21.215	0.000	-7.54e+05	-6.27e+05
Crossing	3316.9424	923.858	3.590	0.000	1506.082	5127.803
Finishing	6892.8765	1208.988	5.701	0.000	4523.130	9262.623
ShortPassing	4598.1326	1978.453	2.324	0.020	720.154	8476.111
Volleys	-6976.9503	1157.245	-6.029	0.000	-9245.274	-4708.626
LongPassing	-3661.1356	1489.028	-2.459	0.014	-6579.790	-742.482
Stamina	-6818.3131	969.916	-7.030	0.000	-8719.452	-4917.175
Vision	-2978.1826	1172.147	-2.541	0.011	-5275.717	-680.649
Marking	-2769.0323	1108.637	-2.498	0.013	-4942.078	-595.986
StandingTackle	5797.5939	1088.344	5.327	0.000	3664.324	7930.864
GKHandling	9518.4253	2407.542	3.954	0.000	4799.385	1.42e+04
Body_Type_C. Ronaldo	-2.184e+07	1.13e+06	-19.249	0.000	-2.41e+07	-1.96e+07
Body_Type_Courtois	1.036e+07	1.13e+06	9.194	0.000	8.15e+06	1.26e+07
Body_Type_Messi	1.321e+07	1.2e+06	11.035	0.000	1.09e+07	1.56e+07
Real_Face_Yes	3.777e+05	3.65e+04	10.348	0.000	3.06e+05	4.49e+05
Position_CDM	9.677e+04	4.09e+04	2.364	0.018	1.65e+04	1.77e+05
Position_GK	-4.247e+05	1.33e+05	-3.187	0.001	-6.86e+05	-1.63e+05
Position_LF	-2.765e+06	2.9e+05	-9.519	0.000	-3.33e+06	-2.2e+06
Position_LS	-2.29e+05	8.46e+04	-2.706	0.007	-3.95e+05	-6.31e+04
Position_RAM	-1.139e+06	2.45e+05	-4.643	0.000	-1.62e+06	-6.58e+05
Position_RF	-1.714e+06	3.23e+05	-5.298	0.000	-2.35e+06	-1.08e+06
weight_group_over	-2.114e+05	6.35e+04	-3.330	0.001	-3.36e+05	-8.7e+04

	Release Clause	predicted
0	8500000.0	9378000.0
1	11500000.0	9418000.0
2	2300000.0	2670000.0
3	1400000.0	1695000.0
4	998000.0	1014900.0
...	...	...
4988	10200000.0	11188000.0
4989	522000.0	443090.0
4990	165000.0	148010.0
4991	979000.0	1155100.0
4992	20800000.0	20046000.0

	Release Clause	actual_predicted
0	8500000.0	9419283.0
1	11500000.0	10069783.0
2	2300000.0	2488346.0
3	1400000.0	1751202.0
4	998000.0	969406.0
...	...	...
4988	10200000.0	10695750.0
4989	522000.0	557240.0
4990	165000.0	177182.0
4991	979000.0	956979.0
4992	20800000.0	19630659.0

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_max_depth	param_max_features	param_min_samples_leaf	param_min_samples_split	param_n_estimators	params	...	split1_test_score	split2_test_score	mean_test_score	std_test_score	rank_test_score	split0_train_score	split1_train_score	split2_train_score	mean_train_score	std_train_score
0	0.442421	0.022022	0.038334	0.002500	8	4	15	12	121	{'max_depth': 8, 'max_features': 4, 'min_sampl...	...	1.000000	1.000000	1.000000	0.00000	1	1.000000	1.000000	1.000000	1.000000	0.000000
1	0.514672	0.007065	0.038077	0.001412	6	5	7	12	137	{'max_depth': 6, 'max_features': 5, 'min_sampl...	...	1.000000	1.000000	1.000000	0.00000	1	1.000000	1.000000	1.000000	1.000000	0.000000
2	0.657688	0.016793	0.050880	0.002381	6	4	8	4	199	{'max_depth': 6, 'max_features': 4, 'min_sampl...	...	1.000000	1.000000	1.000000	0.00000	1	1.000000	1.000000	1.000000	1.000000	0.000000
3	0.476691	0.026048	0.052222	0.003117	6	2	12	7	179	{'max_depth': 6, 'max_features': 2, 'min_sampl...	...	0.999804	0.999912	0.999874	0.00005	10	0.999942	0.999934	0.999933	0.999936	0.000004
4	0.429142	0.013031	0.034635	0.000513	9	5	1	13	107	{'max_depth': 9, 'max_features': 5, 'min_sampl...	...	1.000000	1.000000	1.000000	0.00000	1	1.000000	1.000000	1.000000	1.000000	0.000000